Cryptocurrency Price Prediction Based on Historical Data and Social Media Sentiment Analysis

Abstract

The cryptocurrency space is highly volatile, and predictive systems working in this space are still in their infancy phase. The findings made during an extensive literature survey suggest the lack of a balanced approach and the right combination of data sources, which lead to biased feature sets and discriminative results. These have an impact on the accuracy of the models and skew the classification and prediction results. In this paper, we explore a better approach where a combination of sentiment analysis of social media content, contemporary pricing and market volume data is considered to extract a refined feature set. The features extracted from the preprocessing pipeline will then be used to classify and predict future pricing using a neural network model.